Exploring protein-based films and coatings for active food packaging applications: A comprehensive review
Bibliographic record
Abstract
Proteins-based films and coatings offer an environmentally friendly and sustainable alternative to conventional petroleum-based materials for food packaging applications. These protein-based materials can be extracted from renewable sources, both plant and animal, including soy proteins, whey proteins, corn zein, collagen, and gelatin. As biodegradable and functional food packaging solutions, protein-based films and coatings meet the growing demand for eco-conscious packaging options. These materials boast several benefits, such as effective water and gas barrier properties and the ability to incorporate active ingredients like antimicrobials, oxygen and ethylene scavengers, and antioxidants. These features contribute to extending food shelf life and reducing spoilage. However, protein-based films and coatings face challenges, including sensitivity to moisture and brittleness. These issues can be mitigated through the use of plasticizers, crosslinkers, and nanoparticles. Functionalization of protein-based materials allows them to respond to environmental factors such as pH, temperature, moisture, and gas release, enabling real-time quality monitoring of packaged food. Recent advancements in fabrication techniques have led to improvements in the physicochemical properties of these materials, including enhanced mechanical and barrier characteristics, as well as increased scalability. This review provides an overview of recent research on food packaging applications using plant and animal-derived films and coatings. It examines various characteristics of protein-based films and coatings, as well as the different methods used to process them. The review also explores the life cycle assessment of bio-based materials. The review concludes by addressing current challenges and prospects associated with protein-based materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".